Genetic Research

Who Is At Risk for Alcoholism?

TATIANA
FOROUD, PH.D., is a Chancellor’s Professor in the Department of Medical
and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana.

HOWARD
J. EDENBERG, PH.D., is a Distinguished Professor in the Department of Biochemistry
and Molecular Biology and the Department of Medical and Molecular Genetics, both
at the Indiana University School of Medicine, Indianapolis, Indiana.

JOHN
C. CRABBE, PH.D., is a professor in the Department of Behavioral Neuroscience,
Oregon Health & Science University, and a senior research career scientist
at the VA Medical Center, Portland, Oregon.

The National
Institute on Alcohol Abuse and Alcoholism (NIAAA) was founded 40 years ago to
help elucidate the biological underpinnings of alcohol dependence, including the
potential contribution of genetic factors. Twin, adoption, and family studies
conclusively demonstrated that genetic factors account for 50 to 60 percent of
the variance in risk for developing alcoholism. Case–control studies and
linkage analyses have helped identify DNA variants that contribute to increased
risk, and the NIAAA-sponsored Collaborative Studies on Genetics of Alcoholism
(COGA) has the expressed goal of identifying contributing genes using state-of-the-art
genetic technologies. These efforts have ascertained several genes that may contribute
to an increased risk of alcoholism, including certain variants encoding alcohol-metabolizing
enzymes and neurotransmitter receptors. Genome-wide association studies allowing
the analysis of millions of genetic markers located throughout the genome will
enable discovery of further candidate genes. In addition to these human studies,
genetic animal models of alcohol’s effects and alcohol use have greatly
advanced our understanding of the genetic basis of alcoholism, resulting in the
identification of quantitative trait loci and allowing for targeted manipulation
of candidate genes. Novel research approaches—for example, into epigenetic
mechanisms of gene regulation—also are under way and undoubtedly will further
clarify the genetic basis of alcoholism. Key words: Alcohol dependence;
alcoholism; genetics and heredity; genetic theory of alcohol and other drug (AOD)
use; genetic causes of AOD use, abuse and dependence (genetic AOD); genetic risk
and protective factors; hereditary versus environmental factors; genetic mapping;
Collaborative Studies on Genetics of Alcoholism; human studies; animal studies

Evidence
from archeological artifacts indicates that fermented beverages existed as early
as 10,000 B.C. The excessive consumption of alcohol, however, results in dangers
to the health and well being of the drinker and those around him or her. Today,
the World Health Organization estimates that alcohol causes 1.8 million deaths
(3.2 percent of all deaths) worldwide and 58.3 million (4 percent of total) disability-adjusted
life-years (DALYs)1 [1DALYs are a measure of burden of disease.
One DALY is equal to 1 healthy year of life lost.] lost to disease (http://www.who.int/substance_abuse/facts/alcohol/en/index.html).
In the United States, alcohol dependence (i.e., alcoholism) is a major health
problem, affecting 4 to 5 percent of the population at any given time, with a
lifetime prevalence of 12.5 percent (Hasin et al. 2007).

The National Institute
on Alcohol Abuse and Alcoholism (NIAAA) was founded 40 years ago to further understanding
of the biological underpinnings of alcohol dependence. Early genetic studies were
focused on delineating whether environmental factors, genetic factors, or both
contributed to the risk for alcohol dependence. Once it was apparent that genetics
did indeed play a role in alcohol dependence, NIAAA began to fund studies seeking
to identify relevant genes. Since then, studies in humans and animals have used
complementary approaches to understand the genetics of alcohol use and dependence.
This overview summarizes the evidence supporting a role for genetic factors in
alcoholism and describes how new genetic findings could affect our understanding
of the causes and factors contributing to this debilitating disease and could
potentially guide the development of improved treatments.

EVIDENCE OF
A GENETIC CONTRIBUTION TO ALCOHOL DEPENDENCE

Several study designs, including
twin, family, and adoption studies, are used to determine whether relatively common
diseases, such as alcohol dependence, are caused at least in part by genetic factors
and to estimate the magnitude of the overall genetic contribution. Twin studies
compare the similarity in disease status (i.e., concordance2) [2For
a definition of this and other technical terms, see the glossary, pp. 161–164.]
between identical (i.e., monozygotic) and fraternal (i.e., dizygotic) twins. If
risk for a disease (e.g., alcohol dependence) is determined at least in part by
genetic factors, monozygotic twins, who have identical genetic material (i.e.,
genomes), would be expected to have a higher concordance rate for alcohol dependence
than dizygotic twins, who on average share only half their genome. Studies by
several groups have indeed shown higher concordance rates for alcohol dependence
among monozygotic than among dizygotic twins (Agrawal and Lynskey 2008). Family
studies, which evaluate the members of a family (both alcoholic and nonalcoholic
members) for the presence of the disease, also have provided convincing evidence
that the risk for alcohol dependence is determined partly by genetic influences
(Gelernter and Kranzler 2009). Overall, family, adoption,3 [3Adoption
studies compare the disease status of adoptees with that of their birth parents
(with each of whom they share on average half their genome) and of their adoptive
parents (with whom they typically have no genetic relationship and do not share
their genome).] and twin studies provide convergent evidence that hereditary factors
play a role in alcohol dependence, with variations in genes estimated to account
for 50 to 60 percent of the total variance in risk. These estimates suggest that
although genetic factors are important, nongenetic factors also contribute significantly
to the risk for alcohol dependence.

STRATEGIES FOR IDENTIFYING GENES CONTRIBUTING
TO ALCOHOL DEPENDENCE

Researchers have developed several strategies to
identify genes that contribute to differences in the risk for alcohol dependence,
including case–control studies and linkage analyses. These strategies depend
on the premise that for a particular position in the DNA of these genes, more
than one possible form exists. Each of these forms is termed an allele. The study
methods used to identify genes that affect the risk for alcohol dependence assume
that the presence of certain alleles increases the risk of alcoholism. These variants
that affect risk can be located either directly within a gene or near a gene.

Case–control studies compare allele frequencies in a sample of alcoholic
and control subjects. Because DNA is inherited from both parents, every person
carries two copies of the DNA at a given position in the genome—one allele
that was inherited from the father and one allele that was inherited from the
mother. The genotype describes the variation at a particular position within the
genome and is defined by the allele inherited from the father and the allele inherited
from the mother. If a given allele contributed to the risk for alcohol dependence,
one would expect the allele and/or genotype frequencies to differ between the
case and the control subjects (see figure 1A).

Figure
1. Approaches to identifying genes contributing to the risk of alcoholism.
A) Case–control association study design. Each circle represents a person
who is either an alcoholic (case subject) or not an alcoholic (control subject).
The study assesses the role of a single-nucleotide polymorphism (SNP)* that exists
in two different variants (i.e., alleles)—allele 1 and allele 2. Because
each person inherits two copies of the SNP from their parents, the numbers in
the circles represent the three possible genotypes (11, 12, and 22). Many more
case than control subjects carry at least one copy of allele 1 (i.e., have the
11 and 12 genotypes), suggesting that people with allele 1 may be more likely
to develop alcoholism. B) Linkage study design. A three-generation family tree
(pedigree) is shown. Squares represent male subjects and circles represent female
subjects. Shaded symbols represent alcoholic individuals and unshaded symbols
represent nonalcoholic individuals. In this pedigree, there are alcoholic individuals
in each generation, and both men and women are affected.

NOTE: *An SNP is
a DNA sequence variation occurring when a single nucleotide in a DNA marker (or
other genetic sequence) differs between members of a species or between the chromosome
pairs in an individual.

Initially, case–control
studies often were performed using small numbers of alcoholic and control subjects
and examined the role of a single gene, frequently testing only for a single variation.
This approach has limited power, and many results could not be replicated. The
most robust result from these early studies was the demonstration that the genes
encoding two alcohol-metabolizing enzymes—alcohol dehydrogenase (ADH) and
aldehyde dehydrogenase (ALDH)—played an important role in determining alcoholism
risk (this will be discussed in more detail in the next section).

With
the advances of molecular genetics technologies, it then became possible to scan
the genome using a type of genetic variation called microsatellites. In this approach,
called linkage analysis, the pattern of transmission of a disease (e.g., alcoholism)
in families with multiple affected members is compared with the pattern of transmission
of certain microsatellites (see figure 1B). The underlying hypothesis is that
alcoholics within a family share many risk alleles; therefore, genes containing
alleles that increase the risk for alcoholism reside within chromosomal regions
that are inherited by most or all alcoholic family members. Unfortunately, however,
the chromosomal regions that were identified using this approach often contained
hundreds or even thousands of genes, making it very challenging to determine which
specific gene(s) contribute to the risk for alcoholism.

The Collaborative
Studies on Genetics of Alcoholism Study

Another major advancement
in the search for genes contributing to the risk for alcoholism was the initiation
in 1989 of the NIAAA-funded Collaborative Studies on Genetics of Alcoholism (COGA),
a family study with the expressed goal of identifying contributing genes using
newly available genetic technologies (Begleiter et al. 1995; Bierut et al. 2002;
Edenberg 2002). The study was groundbreaking in several ways, including its size,
emphasis on families, and extensive characterization of subjects. In the process,
COGA researchers developed a novel assessment instrument, the Semi-Structured
Assessment of the Genetics of Alcoholism (SSAGA), which since has been translated
into nine languages and is used by over 237 investigators worldwide in studies
of alcohol use and dependence.

Families were obtained by recruiting alcohol-dependent
probands (i.e., index cases) who were in treatment and who gave permission to
contact their family members. This approach generated a dataset of 1,857 families
consisting of 16,062 individuals as of March 2010. Moreover, the researchers identified
a genetically informative subset comprising 262 families with at least three first-degree
relatives who met lifetime criteria for both Diagnostic and Statistical Manual
of Mental Disorders, Third Edition, Revised (DSM–III–R) (American
Psychiatric Association 1987) alcohol dependence and Feighner definite alcoholism;4
[4These criteria, which were the accepted diagnostic criteria at the
time of COGA’s initiation, were based on the definitions established in
the DSM–III–R (American Psychiatric Association 1987) and by Feighner
and colleagues (1972).] this subset became the focus of genetic analyses. The
extensive characterization of subjects also allowed analysis of the role of hereditary
characteristics (i.e., endophenotypes) that often are associated with alcoholism
but are not direct symptoms of alcoholism, such as certain electrophysiological
traits, drug dependence, other related psychiatric conditions, and personality
measures (Edenberg 2002).

Genetic analyses in this subsample of the COGA
dataset have implicated several different chromosomal regions as possibly containing
one or more genes contributing to alcohol dependence; to related clinical characteristics
(i.e., phenotypes) such as smoking, depression, suicidal behavior, conduct disorder,
and the largest number of drinks within a 24-hour period; and to neurobiological
endophenotypes such as event-related potentials and brain oscillations in electrophysiological
activity (Edenberg 2002; Edenberg and Foroud 2006). Despite much progress, however,
identification of the specific genes contributing to these phenotypes remains
a challenging task because they lie within broad linkage regions that often encompassed
10 to 30 million base pairs.

In addition to COGA, NIAAA has supported several
other large family studies designed to identify genes contributing to the risk
for alcohol dependence. These include a large study in Ireland that is recruiting
siblings (Kendler et al. 1996; Prescott et al. 2005), a family study of both alcohol
dependence and alcohol-related endophenotypes (including electrophysiological
measures, similar to COGA) (Hill 1998), and a study of Mission Indian families
(Ehlers et al. 2004). Twin studies also have remained a focus of several NIAAA-funded
research projects (Jacob et al. 2001; Madden et al. 2000). Moreover, a study of
offspring of alcoholic fathers has expanded into a longitudinal, multigenerational
genetic study that is focused on better understanding the factors contributing
to the initiation of alcohol use as well as the long-term risk for alcohol dependence
(Schuckit 1991). Finally, studies also have examined African-American alcohol-dependent
families ascertained on the basis of cocaine or opioid dependence (Gelernter and
Kranzler 2009). Together, these approaches, although by no means completed, already
have resulted in the identification of some genes that impact the risk for alcohol
dependence. Some of these genes and the proteins they encode are discussed in
the next section.

GENES CONTRIBUTING TO ALCOHOL DEPENDENCE

Genes
Encoding Alcohol-Metabolizing Enzymes

Classic studies, which have
been replicated in many populations, have demonstrated that certain coding variations
in two genes affecting alcohol metabolism have a strong protective effect—that
is, they both substantially lower the risk for alcoholism. These variants affect
a gene called ADH1B, which encodes a variant of ADH, and a gene called
ALDH2, which encodes a variant of ALDH (Edenberg 2000, 2007; Hurley et
al. 2002) (figure 2). The protective variant in the ALDH2 gene, known
as ALDH2*2, involves a point mutation that results in the exchange of
the amino acid glutamate at position 487 of the ALDH protein for the amino acid
lysine. This mutation acts in a nearly dominant manner to render the enzyme almost
inactive: even people who inherit only one copy of ALDH2*2 and one “normal”
copy of the gene (i.e., people who are heterozygous for this mutation) produce
an ALDH enzyme with extremely low enzyme activity (Crabb et al. 1989). As a result,
these individuals exhibit highly elevated levels of acetaldehyde, which produces
aversive reactions, including flushing, elevated heart rate (i.e., tachycardia),
and nausea after consuming even a small amount of alcohol (Eng et al. 2007). Similarly,
coding variations in the ADH1B gene (called ADH1B*2 and ADH1B*3)
that encode highly active enzymes which increase the rate at which acetaldehyde
is produced also are strongly protective and reduce the risk for alcohol dependence
(Edenberg 2007; Thomasson et al. 1991).

Figure
2. The main steps of alcohol metabolism. Alcohol first is metabolized
to acetaldehyde by the enzyme alcohol dehydrogenase (ADH), which is encoded by
several genes, each of which may exist in several variants (i.e., alleles). Certain
alleles encode ADH molecules that result in the metabolism of alcohol (denoted
by the red arrow above ADH). As a result, buildup of acetaldehyde occurs (denoted
by the upward-pointing arrow), leading to such aversive effects as nausea, flushing,
and accelerated heart beat (i.e., tachycardia). The acetaldehyde then is metabolized
to acetate by the enzyme aldehyde dehydrogenase (ALDH), which also is encoded
by several genes existing in different alleles. Certain alleles in the ALDH2 gene,
which encodes a key ALDH enzyme, can result in very low activity of the enzyme
(denoted by the black arrow with a red line through it), again causing acetaldehyde
accumulation and the resulting aversive effects.

These
gene variations have been selected for in different populations. For example,
the ALDH2*2 variant is common only among people from east Asia, the ADH1B*2
variant is common among people from east Asia and the Middle East, and the ADH1B*3
variant is common in people from Africa (Edenberg 2007; Eng et al. 2007;
Li et al. 2007, 2009). All of these variations have strikingly strong effects
on risk; thus, in Asian populations, ALDH2*2 and ADH1B*2 each
can lower risk by two- to sevenfold. No other known gene variations have such
a strong effect on risk for alcoholism.

The influence of ADH variations
on risk was further investigated through linkage studies performed in non-Asian
families. These analyses detected linkage of alcoholism to a broad region on chromosome
4q that included the ADH gene cluster (Long et al. 1998; Prescott et
al. 2006; Reich 1996; Reich et al. 1998; Williams et al. 1999). Given the strong
prior evidence for the role of the ADH genes in alcoholism susceptibility,
the COGA investigators initially focused on the 262 families from the study with
a very strong history of alcoholism. In these families, they determined the genotype
for 110 DNA markers known as single-nucleotide polymorphisms (SNPs), which were
distributed throughout the ADH gene cluster. These analyses detected
significant evidence of association of alcoholism with 12 SNPs located in and
around the ADH4 gene (Edenberg et al. 2006) and modest evidence of association
with noncoding SNPs5 [5Noncoding SNPs are DNA sequence variations
that are located in regions of the ADH gene that do not encode the actual ADH
protein.] in ADH1A and ADH1B. Moreover, the analyses provided
evidence that the ADH1B*3 allele was protective among African-American
families (Edenberg et al. 2006). The association of several noncoding ADH polymorphisms
with alcohol dependence has been replicated in other studies (Edenberg 2007; Macgregor
et al. 2009).

Genes Encoding γ-Aminobutyric Acid Receptors

The
brain-signaling molecule (i.e., neurotransmitter) γ-aminobutyric acid (GABA),
by interacting with a molecule called the GABA-A receptor, mediates several effects
of alcohol, including alcohol’s sedative and anxiety-reducing (i.e., anxiolytic)
effects, motor incoordination, tolerance, and dependence (Kumar et al. 2009).
Several genes that encode subunits of the GABA-A receptor are associated with
an increased risk for alcoholism. For example, significant evidence suggests that
a gene called GABRA2, which with other GABA-A receptor genes is located
in a cluster on chromosome 4, is associated with alcoholism (Edenberg et al. 2004).
This finding has been replicated in many (but not all) case–control studies
in Europeans, Australians, and Plains Indians (Edenberg and Foroud 2006; Gelernter
and Kranzler 2009). In several samples, the association with GABRA2 was
greatest among those alcohol-dependent people who also were dependent on nicotine
(Philibert et al. 2009) or illicit drugs (Agrawal et al. 2006; Philibert et al.
2009); the latter subgroup is characterized by greater severity of alcohol problems
in general (Dick et al. 2007). In addition, another gene within the chromosome
4 GABA-A cluster, GABRG1, also may influence the risk for alcoholism
(Covault et al. 2008; Enoch et al. 2009).

Finally, GABA-A genes on other
chromosomes, including GABRG3 on chromosome 15 (Dick et al. 2004) and
GABRA1 on chromosome 5 (Dick et al. 2006), also have been associated
with alcoholism. However, these associations have not yet been replicated in other
samples and therefore must be considered tentative.

Genes Encoding
Acetylcholine Receptors

Another neurotransmitter system involved in
the actions of alcohol is acetylcholine, which can interact with different types
of receptors, including muscarinic and nicotinic receptors. As with the GABA-A
receptor, the subunits for each of these receptors are encoded by different genes
that have several different alleles (i.e. code for different forms of the receptor
subunit), and certain alleles have been associated with an increased risk for
alcoholism. For example, the gene that encodes the muscarinic acetylcholine receptor
subtype 2, called CHRM2, appears to be an important risk factor for alcohol
dependence. The receptor encoded by this gene is a G-protein–coupled receptor6
[6G-protein–coupled receptors interact with a signaling molecule
(e.g., acetylcholine) outside the cell, resulting in the activation of signaling
pathways within the cell and thereby inducing a cellular response. Specifically,
binding of the receptor to the signaling molecule alters the structure of the
receptor so that it can activate an associated G-protein, which in turn can act
on other proteins in the cell.] involved in many functions. In the COGA study,
SNPs in CHRM2 were associated with alcohol dependence, a finding that
was replicated in an independent study (Edenberg and Foroud 2006).

Extensive
research also has examined the neuronal nicotinic acetylcholine receptors (nAChRs),
which are affected by both nicotine and alcohol. DNA variation in the genes that
encode the subunits of these receptors may play a role in the susceptibility to
alcohol dependence and nicotine addiction. Similar to the GABA-A receptors, the
genes encoding these receptors are found in clusters on several chromosomes. Studies
have reported an association of SNPs in CHRNA5–CHRNA3 (Wang et
al. 2009) and CHRNA6–CHRNB3 (Hoft et al. 2009) gene clusters with
alcohol dependence or alcohol consumption.

Genome-wide Association
Studies

In the past few years, it has become possible to genotype
up to a million SNPs throughout the genome in a single experiment—an approach
called genome-wide association studies (GWASs). This technique, which is based
on the assumption that common genetic variation contributes to disease risk, allows
a comprehensive test of association across the genome, rather than testing only
one gene at a time. It has been used for many different diseases, with varying
success. In particular, the relatively low statistical power of GWASs is a significant
hurdle. Thus, the analyses require very large samples because most variations
only have small effects; moreover, the multiple testing involved in a GWAS reduces
the statistical power to detect associations.

Several studies recently
have reported GWAS results from case–control studies comparing alcohol-dependent
case subjects to nondependent control subjects. The first published study, conducted
in Germany, compared 487 men in inpatient treatment for alcohol dependence to
1,358 control subjects (Treutlein et al. 2009). The study identified several SNPs
in a region on chromosome 2 that previously had been linked to alcohol dependence,
as well as SNPs in a gene called CDH13 that is located on chromosome
16 and the ADH gene ADH1C on chromosome 4.

Recently, COGA reported
results of a GWAS that included 847 alcohol-dependent case and 552 control subjects
(Edenberg et al. 2010). The combined evidence from this case–control study,
a follow-up in families, and gene expression data provided strongest support for
the association with alcohol dependence of a cluster of genes on chromosome 11.7
[7The genes located in this cluster are SLC22A18, PHLDA2, NAP1L4, snora54,
CARS, and OSBPL5.] However, the associations detected in the COGA GWAS did not
reach the threshold for statistical significance for this type of analysis, and
therefore additional studies must be conducted to further define the associated
genes. Several SNPs nominated as candidates in the earlier German GWAS also were
replicated in the COGA sample, including SNPs in or near the genes CPE,
DNASE2B, SLC10A2, ARL6IP5, ID4, GATA4,
SYNE1, and ADCY3.

Another recent report (Bierut et al.
2010) described a GWAS using an overlapping set of COGA subjects as well as additional
subjects recruited as part of other addiction research projects. This sample included
both African-American and European-American subjects, and the primary analysis
sought to identify association with alcohol dependence using a case–control
design. Although none of the detected associations met genome-wide criteria for
statistical significance, there was some evidence to support the previously reported
association in GABRA2 as well as in a gene called ERAP1, which
encodes the enzyme endoplasmic reticulum aminopeptidase 1 (Bierut et al. 2010).
Finally, a GWAS in a sample of twins and their families recruited in Australia
is currently being analyzed.

GENETIC ANIMAL MODELS OF ALCOHOL’S EFFECTS
AND ALCOHOL USE

Since the earliest days of alcohol research, the use of
animal models has featured strongly in attempts to understand genetic contributions
to the mechanisms through which alcohol exerts its biological effects and to individual
differences in risk for alcohol dependence. The main advantage of animal models
for these genetic analyses is that they allow researchers to more tightly control
environmental influences, thereby making it easier to identify genetic risk factors.

In 1959, inbred mouse strains first were shown to differ in their tendency
to drink alcohol (McClearn and Rodgers 1959), and studies with inbred strains
continue to this day. Each inbred strain possesses a random collection of genes
(i.e., genotype), but all the animals within a strain are genetically identical.
This reduction in the genetic variation among the animals studied could increase
the power to identify genes contributing to alcohol-related traits.

Another
commonly used type of animal model involves selectively bred lines. Starting in
the late 1940s, researchers in Chile bred rats that preferred to drink alcohol-containing
solutions as well as rats that avoided alcohol (Mardones and Segovia-Riquelme
1983). Such selective breeding has been repeated numerous times with rats and
mice, resulting in pairs of animal lines that differ with respect to a particular
alcohol-related trait. A list of currently available rodent selected lines is
shown in table 1. Studies with the high- and low-drinking selected lines in particular
have been a major focus of NIAAA-sponsored research efforts (for a review, see
Crabbe et al. 2010; other reviews were published in a special issue of Addiction
Biology, Vol. 11[3–4], 2006). Animals have been selected for many alcohol-related
traits, including preference for alcohol, tolerance or sensitivity to alcohol’s
effects, and withdrawal severity. New selection projects also are emerging; for
example, researchers are breeding mice that exhibit binge-like drinking (Crabbe
et al. 2009).

Studies with these selected lines have contributed
a great deal to understanding the neurobiological bases for alcohol’s myriad
effects. For example, researchers consistently have observed low levels of the
neurotransmitter serotonin in certain brain areas (i.e., the limbic system) and
other indications of dysregulation of the serotonin system in animal lines bred
for high alcohol drinking (Crabbe 2008). Other studies with selected lines have
shown dysregulation of the GABA and glutamate systems in animals bred to exhibit
severe withdrawal. (Finn et al. 2004).

CONTRIBUTIONS OF GENETIC ANIMAL
MODEL RESEARCH

Enhanced Understanding of Alcohol’s Pharmacology
and Neurobiology

Animal research has been invaluable for discovering
how alcohol exerts its biological effects. For example, numerous studies have
shown an important role for GABA neurotransmission in mediating alcohol’s
acute and chronic effects (Finn et al. 2004; Lobo and Harris 2008; Kumar et al.
2009). Additional animal studies have demonstrated that alcohol’s pharmacology
involves nearly all major neurotransmitter targets, including the glutamate/NMDA,8
[8The N-methy-D-aspartate receptor is one of the receptor
types for the neurotransmitter glutamate.] serotonin, dopamine, norepinephrine,
and cannabinoid receptor systems (Kelai et al. 2006; Smith et al. 2008; Vengeliene
et al. 2005). By acting on all these signaling systems, alcohol ultimately exerts
its effects through modulation of intracellular signaling cascades (Newton and
Messing 2006). Without animal models, researchers could not have gained an understanding
of these highly complex mechanisms underlying alcohol’s diverse effects,
and genetic animal models in particular have aided in understanding individual
differences in sensitivity to these effects.

Gene Identification and
Quantitative Trait Locus Mapping

Animal models also have been exploited
for many years in attempts to identify specific gene variations associated with
increased sensitivity to alcohol’s effects. These gene-mapping studies,
which commenced in the early 1990s, have used methods similar to those described
above for human studies (e.g., linkage analyses). They primarily have sought to
identify quantitative trait loci (QTLs)—DNA regions that are associated
with characteristics (i.e., quantitative traits) which vary in the degree to which
they are present (e.g., sensitivity to alcohol or height). Such traits typically
are determined by multiple genes and each QTL may contain one or more of these
genes. Compared with humans, studies with rats and mice have the distinct advantage
that researchers can use individuals with defined genotypes and control patterns
of mating, making it much easier to localize the chromosome region of interest
(i.e., the “locus” of the QTL). The most recent systematic review
(Crabbe et al. 1999) of the alcohol-related QTL data for the various alcohol-related
traits being mapped, which now is out of date, listed the likely locations of
several genes affecting alcohol withdrawal severity, preference for drinking,
and sensitivity to alcohol’s effects. Researchers at the Oregon Health &
Science University now maintain a much more recent update of mouse alcohol QTL
locations for these and other alcohol-related traits, which can be accessed via
the Portland Alcohol Research Center Web site (http://www.ohsu.edu/parc/).

The greatest success story for alcohol-related QTL mapping in rodents has
been the discovery of a quantitative trait gene (QTG)9 [9In
contrast to a QTL, which only identifies a DNA region that is likely to contain
a gene contributing to a quantitative trait (but also may contain other, unrelated
DNA sequences), a QTG represents the actual gene.] that affects acute withdrawal
severity from both alcohol and pentobarbital in mice. Originally, investigators
mapped several QTLs contributing to this trait to locations on various mouse chromosomes
(Buck et al. 1997). Subsequent studies with a variety of specifically created
genetic animal models gradually narrowed down the size of the DNA region (i.e.,
reduced the confidence interval) around one of these QTLs until only a few genes
remained within the confidence interval. Functional studies then demonstrated
that the most likely gene contributing to the trait was Mpdz, which encodes
a protein containing multiple structural components known as PDZ-domains (Shirley
et al. 2004). Studies of this gene’s pattern of expression in the brain
and of the functions of the MPDZ protein continue, as do studies to identify the
receptor molecules with which MPDZ interacts (e.g., the serotonin 2C receptor)
(Chen et al. 2008a; Reilly et al. 2008).

Additional mapping studies
aim to narrow other QTLs for alcohol responses, both in animals (Bennett et al.
2007, 2008; Hitzemann et al. 2009) and in humans. A recent comparison of data
from mouse and human QTL mapping identified a promising region of human chromosome
1 that was linked to alcohol dependence and which overlapped with an area of mouse
chromosome 1 that has been linked to an alcohol withdrawal QTL (Ehlers et al.
2010).10 [10Although humans and mice have different numbers
of chromosomes and substantial variation in their genome, there are some parallels
between the two genomes. Thus, about 80 percent of genes that are located closely
together on a human chromosome also tend to be located in a cluster on a mouse
chromosome.] However, as described by Ehlers and colleagues (2010) a detailed
comparison of rodent and human maps to see whether the QTLs from rodent studies
identify the same chromosomal regions as the linkage studies in humans is very
difficult. Nevertheless, some promising results of cross-species consistency exist,
which likely will increase in number as the details of both rodent and human genetic
maps improve.

Classical QTL analysis has associated individual differences
in gene sequence (or in other genetic markers, such as microsatellites) with differences
in the phenotype being mapped. A recent development in rodent QTL mapping has
been development of expression QTL (eQTL) mapping. eQTLs are DNA regions that
differ not in their gene sequence, but in the level to which the gene becomes
active (i.e., is expressed) in individuals differing with respect to certain alcohol-related
traits. This information can be gathered from microarray experiments that measure
the levels of individual mRNAs. These additional eQTLs greatly expand the pool
of potentially informative genes.

The eQTL approach has been used to compare
gene expression in brain tissue from several rodent lines and strains genetically
predisposed to drink alcohol with control tissue from low-drinking animals. The
chromosomal location of differentially expressed genes then was compared with
QTL data based on genetic sequence variations (i.e., polymorphisms). This combination
of information suggested several candidate genes that may influence alcohol drinking
(Mulligan et al. 2006; Weng et al. 2009).

An additional refinement to the
gene-finding efforts has been the study of networks of proteins or the genes that
encode them. The reasoning is that even if different studies (or studies in different
species) do not identify the same specific gene as being involved in a trait,
they might identify a network of genes that underlies the genetic “signal”
across studies and datasets and which encodes proteins that have similar functions
or are involved in similar pathways (e.g., McBride et al. 2009).

Candidate
Gene Studies and Gene Targeting

Another important development enhancing
the possibilities of genetic animal models of alcoholism was the development of
transgenic animals in the late 1980s. These are animals that have been genetically
modified so that the expression of a single candidate gene has been selectively
inactivated or augmented compared with the parent strain. This approach allows
researchers to study the influence of individual genes on risk for alcoholism
(or many other diseases or behaviors). By now, more than 100 candidate genes have
been studied for their contribution to alcohol’s effects, usually by comparing
mice in which a single gene has been inactivated (i.e., knockout mice) with control
mice in which the gene still is functional. As reviewed by Crabbe and colleagues
(2006), most of the genes thus studied were found to influence some aspect of
alcohol sensitivity. For example, of 84 different transgenic animals tested for
effects on alcohol self-administration, one-quarter exhibited increased drinking,
one-third exhibited decreased drinking, and 40 percent did not differ from control
animals (Crabbe et al. 2006). This finding clearly demonstrates the multiplicity
of genetic influences on alcohol responses. As gene-targeting technologies allow
more specific experimental regulation of genes than simple deactivation or overexpression,
these approaches will continue to provide important data. For example, researchers
now can manipulate genes so that they are expressed only in certain cell types
or during particular developmental periods.

Candidate gene studies also
have been valuable in looking for consistency across species in the impact of
certain genes or gene variants. Invertebrate models (e.g., the fruit fly Drosophila
or the worm Caenorhabditis elegans) offer much more powerful tools to
manipulate the genome than do rodents (Lovinger and Crabbe 2005). However, to
be able to use such models, researchers first need to demonstrate that corresponding
genes exist in these organisms and that they actually have similar functions.
One example of such convergence of evidence is the finding that a small signaling
molecule called neuropeptide Y (NPY) and its receptors play a role in alcohol
intoxication in mice, rats, and Drosophila (Chen et al. 2008b;
Gilpin et al. 2004; Thiele et al. 2002). A meta-analysis of human association
data, in contrast, found no clear evidence that polymorphisms in the gene encoding
a precursor of NPY are associated with alcohol dependence (Zhu et al. 2003). However,
some genes encoding NPY receptors may play a role in alcohol dependence and withdrawal
(Wetherill et al. 2008). Finally, certain signaling proteins (e.g., epidermal
growth factor receptor [EGFR], protein kinase C, protein kinase A, and cyclic
AMP [cAMP]) have been implicated in alcohol’s effects across multiple species,
including humans, rats, mice, Drosophila, and zebrafish (Corl et al.
2009; Newton and Messing 2006, 2007; Peng et al. 2009).

STUDIES OF GENE–ENVIRONMENT
INTERACTION

Studies clearly have shown that both genetic and environmental
factors contribute to the risk for alcohol dependence, and it is likely that the
interplay between these factors is critical for determining the risk for alcohol
abuse and dependence. Advances in genetic technologies already have allowed researchers
to explore the genome in ever greater detail, and with the advent of whole-genome
sequencing, complete delineation of genetic variation soon will be available.
In contrast, our understanding of the critical environmental factors influencing
alcohol use disorders remains inadequate and is an area of active research. One
of the challenges is how to define the environment, which may include family,
peer, and societal influences; other exposures; personality or psychiatric factors
(which also have genetic components); and many more, most of which change over
time. Furthermore, the influence of these factors on the risk of alcohol use disorders
varies within the lifespan (Sher et al. 2010; van der Zwaluw and Engels 2009).

Animal
models offer significant advantages for studies attempting to tease apart genetic
and environmental influences on an individual’s risk for alcoholism. Given
their methodological power, it is surprising how little research into this area
has been done using genetic animal models. One trait that has been found to be
genetically determined is alcohol preference of inbred mouse strains. Thus, specific
mouse strains have displayed their tendencies to drink more or less alcohol by
choice repeatedly across 50 years of studies. In fact, alcohol preference in these
animals is even more replicable across studies (and therefore, across environments)
than brain weight (Wahlsten et al. 2006), suggesting that it is strongly influenced
by genetic effects. Not all alcohol traits are so stable, however, and the combined
effects of genetic and environmental manipulations could be exploited more fully
using genetic animal models.

A recent review has discussed several important
features of gene–environment interaction research (Sher et al. 2010). For
example, the social environment plays such a crucial role in shaping drinking
behaviors in humans, but it is difficult to identify corresponding rat and mouse
behaviors and environmental factors. One example of a study analyzing gene–environment
interactions in animals (Hansson et al. 2006) compares the influence of environmental
stress in a rat line selectively bred for high alcohol preference (i.e., the Marchigian-Sardinian
preferring rats) with their nonselected control group. The investigators found
that the genetically “enriched” rats were more sensitive than the
control animals to the effects of environmental stress on reinstatement of previously
extinguished alcohol drinking (i.e., the alcohol-preferring rats resumed alcohol
drinking more easily after being exposed to a stressor). Moreover, the differences
resulted, at least in part, from variations between high-drinking and low-drinking
animals in a gene encoding a receptor for corticotropin-releasing hormone (CRH)
(which is involved in the body’s stress response) and in the expression
of that gene (Hansson et al. 2006). Thus, this study demonstrated an interaction
between a specific genotype and an environmental factor (i.e., stress).

Analysis
of human gene–environment interactions also are complicated by the fact
that these interactions are important from adolescent exposure to alcohol and
then throughout life. Accordingly, from a developmental perspective, the critical
environmental influences are likely to change over time (e.g., the relative influence
of family versus peer factors). Studies that follow genetically specified animals
prospectively while extracting biological information at different times along
the way are a promising area for future research that has not been sufficiently
exploited thus far.

FUTURE DIRECTIONS

Research into the genetics
of alcoholism, both in humans and in animal models, has made great strides over
the past four decades, and even more approaches are beginning to be evaluated.
For example, there is growing interest in studying epigenetic factors—that
is, factors which alter certain phenotypes by modifying regulation of gene expression,
without, however, changing the gene’s DNA sequence. One such factor that
can impact gene expression is methylation of the DNA. Other epigenetic changes
alter the packaging of DNA into chromatin. For example, two enzyme families called
histone acetyltransferases and deacetylases can be used to alter chromatin structure
experimentally, and studies found that when such changes accompany chronic drug
administration, they can modify cocaine-related behaviors in rats (Renthal and
Nestler 2009). Although similar research on alcohol-related traits still is in
its infancy, some studies have found that alcoholic patients exhibited greater
levels of DNA methylation of two different genes than nonalcoholics and, consequently,
greater reduction in the expression of those genes (Bleich et al. 2006; Bonsch
et al. 2005).

MicroRNAs—short RNA molecules naturally encoded by
the genome that can bind to certain mRNA molecules, thereby repressing the further
processing of these mRNAs—also might be involved in regulating alcohol’s
effects (Miranda et al. 2010). These microRNAs also offer a new experimental method
for silencing the expression of specifically targeted genes. The expression of
microRNAs is sensitive to epigenetic modulation, and turning microRNAs on or off
has become feasible in rodent models. Modification of microRNAs may offer a new
pathway for identifying critical genes that can then serve as target for new therapeutic
drugs for alcoholism treatment.

In summary, the genetics field has undergone
a technological revolution, particularly in the past decade, allowing researchers
to process large numbers of samples for their genetic studies and to efficiently
interrogate the entire genome. Using these strategies, researchers have been able
to identify a number of genes in which variations appear to contribute to the
susceptibility to alcohol dependence. It is important to note, however, that the
individual role of each of these genes, and the SNPs within them, is quite modest.
This means that a given allele or SNP that has been found to be associated with
alcohol dependence may increase the risk of alcoholism only incrementally. As
a result, it would be a gross overinterpretation of the results obtained in human
association studies to date to suggest that we currently have a means to identify
people at greatest risk for alcohol dependence. With the exception of the strong
protective effects of certain ADH and ALDH variants, each gene variant identified
to date has a much smaller individual effect on alcoholism risk than, for example,
a family history of alcoholism.

Another challenge is to relate the complex
human behavioral phenotypes to specific variations in the sequence and expression
of specific genes and, perhaps more importantly, to the function of the proteins
encoded by these genes. The answers may come from networks of genes that encode
proteins of similar function, rather than from specific genes individually. Examining
such networks represents another level of complexity that poses a huge quantitative
challenge, computationally and statistically. However, researchers also are making
substantial progress on this bioinformatics front, and the continuing development
of greatly enhanced bioinformatics capacity is increasing the power of studies
in both rodent models and humans.

The identification of any genes that appear
important in alcoholism susceptibility provides an opportunity to better understand
the biological pathways involved in alcohol’s actions. It also may yield
important insights that will allow the development of better pharmacological treatments
to help those who wish to reduce their alcohol consumption. All such potential
new therapies will of course be tested first in animal models (Egli 2005), and
the coordination of animal model and human research therefore will continue to
be an important theme for alcohol research for many years to come.

ACKNOWLEDGEMENTS

Preparation of this manuscript was supported by National Institutes of
Health grants U10AA008401, P60AA007611, P60AA10760, U01AA13519, R37AA06460, U01AA016660,
and R21AA017941 and the Department of Veterans Affairs.

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